From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations

• URL: http://arxiv.org/abs/2509.16584v1
• Date: Sat, 20 Sep 2025 09:10:26 GMT
• Title: From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations
• Authors: Benlu Wang, Iris Xia, Yifan Zhang, Junda Wang, Feiyun Ouyang, Shuo Han, Arman Cohan, Hong Yu, Zonghai Yao,
• Abstract summary: Large language models (LLMs) have demonstrated promising performance on medical benchmarks.<n>However, their ability to perform medical calculations remains underexplored and poorly evaluated.<n>In this work, we revisit medical calculation evaluation with a stronger focus on clinical trustworthiness.
• Score: 45.414878840652115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated promising performance on medical benchmarks; however, their ability to perform medical calculations, a crucial aspect of clinical decision-making, remains underexplored and poorly evaluated. Existing benchmarks often assess only the final answer with a wide numerical tolerance, overlooking systematic reasoning failures and potentially causing serious clinical misjudgments. In this work, we revisit medical calculation evaluation with a stronger focus on clinical trustworthiness. First, we clean and restructure the MedCalc-Bench dataset and propose a new step-by-step evaluation pipeline that independently assesses formula selection, entity extraction, and arithmetic computation. Under this granular framework, the accuracy of GPT-4o drops from 62.7% to 43.6%, revealing errors masked by prior evaluations. Second, we introduce an automatic error analysis framework that generates structured attribution for each failure mode. Human evaluation confirms its alignment with expert judgment, enabling scalable and explainable diagnostics. Finally, we propose a modular agentic pipeline, MedRaC, that combines retrieval-augmented generation and Python-based code execution. Without any fine-tuning, MedRaC improves the accuracy of different LLMs from 16.35% up to 53.19%. Our work highlights the limitations of current benchmark practices and proposes a more clinically faithful methodology. By enabling transparent and transferable reasoning evaluation, we move closer to making LLM-based systems trustworthy for real-world medical applications.

Related papers

• When Metrics Disagree: Automatic Similarity vs. LLM-as-a-Judge for Clinical Dialogue Evaluation [18.338933046286257]
  Large language models (LLMs) are increasingly employed to address diverse problems, including medical queries.<n>LLMs often perform poorly in medical contexts, potentially leading to harmful misguidance for users.<n>This paper focuses on fine-tuning the Llama 2 7B, a transformer-based, decoder-only model, using transcripts from real patient-doctor interactions.
  arXiv  Detail & Related papers  (2026-02-27T21:09:43Z)
• LiveMedBench: A Contamination-Free Medical Benchmark for LLMs with Automated Rubric Evaluation [22.211535340726073]
  LiveMedBench is a continuously updated benchmark that harvests real-world clinical cases from online medical communities.<n>LiveMedBench comprises 2,756 real-world cases spanning 38 medical specialties and multiple languages, paired with 16,702 unique evaluation criteria.<n>Extensive evaluation reveals that even the best-performing model achieves only 39.2%, and 84% of models exhibit performance degradation on post-cutoff cases.
  arXiv  Detail & Related papers  (2026-02-10T23:38:25Z)
• Automated Rubrics for Reliable Evaluation of Medical Dialogue Systems [19.880569341968023]
  Large Language Models (LLMs) are increasingly used for clinical decision support, where hallucinations and unsafe suggestions may pose direct risks to patient safety.<n>We propose a retrieval-augmented multi-agent framework designed to automate the generation of instance-specific evaluation rubrics.
  arXiv  Detail & Related papers  (2026-01-21T16:40:41Z)
• Toward Reliable Clinical Coding with Language Models: Verification and Lightweight Adaptation [3.952186976672079]
  We show that lightweight interventions, including prompt engineering and small-scale fine-tuning, can improve accuracy without the computational overhead of search-based methods.<n>To address hierarchically near-miss errors, we introduce clinical code verification as both a standalone task and a pipeline component.
  arXiv  Detail & Related papers  (2025-10-08T23:50:58Z)
• LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation [58.25892575437433]
  evaluating large language models (LLMs) in medicine is crucial because medical applications require high accuracy with little room for error.<n>We present LLMEval-Med, a new benchmark covering five core medical areas, including 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios.
  arXiv  Detail & Related papers  (2025-06-04T15:43:14Z)
• ChestX-Reasoner: Advancing Radiology Foundation Models with Reasoning through Step-by-Step Verification [57.22053411719822]
  ChestX-Reasoner is a radiology diagnosis MLLM designed to leverage process supervision mined directly from clinical reports.<n>Our two-stage training framework combines supervised fine-tuning and reinforcement learning guided by process rewards to better align model reasoning with clinical standards.
  arXiv  Detail & Related papers  (2025-04-29T16:48:23Z)
• Med-CoDE: Medical Critique based Disagreement Evaluation Framework [72.42301910238861]
  The reliability and accuracy of large language models (LLMs) in medical contexts remain critical concerns.<n>Current evaluation methods often lack robustness and fail to provide a comprehensive assessment of LLM performance.<n>We propose Med-CoDE, a specifically designed evaluation framework for medical LLMs to address these challenges.
  arXiv  Detail & Related papers  (2025-04-21T16:51:11Z)
• GEMA-Score: Granular Explainable Multi-Agent Scoring Framework for Radiology Report Evaluation [7.838068874909676]
  Granular Explainable Multi-Agent Score (GEMA-Score) conducts both objective and subjective evaluation through a large language model-based multi-agent workflow.<n>GEMA-Score achieves the highest correlation with human expert evaluations on a public dataset.
  arXiv  Detail & Related papers  (2025-03-07T11:42:22Z)
• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references.<n>We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey.<n>Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions.<n>We propose a novel approach utilizing structured medical reasoning.<n>Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.